Download: Get TCGA dataDownload: Get GEO dataAnalysis: To analyze TCGA dataDPA: Differential Phenotype AnalysisLPA: Literature Phenotype AnalysisFEA: Functional Enrichment AnalysisFEAplot: Functional Enrichment Analysis PlotGRN: Gene Regulatory NetworkURA: Upstream Regulator AnalysisPRA: Pattern Regognition AnalysisplotNetworkHive: GRN hive visualization taking into account Cosmic cancer genesplotURA: Upstream regulatory analysis plotplotCircos: Moonlight Circos PlotIn order to make light of cancer development, it is crucial to understand which genes play a role in the mechanisms linked to this disease and moreover which role that is. Commonly biological processes such as proliferation and apoptosis have been linked to cancer progression. Based on expression data we perform functional enrichment analysis, infer gene regulatory networks and upstream regulator analysis to score the importance of well-known biological processes with respect to the studied cancer. We then use these scores to predict two specific roles: genes that act as tumor suppressor genes (TSGs) and genes that act as oncogenes (OCGs). This methodology not only allows us to identify genes with dual role (TSG in one cancer type and OCG in another) but also to elucidate the underlying biological processes.
Cancer development is influenced by mutations in two distinctly different categories of genes, known as tumor suppressor genes (TSG) and oncogenes (OCG). The occurrence of mutations in genes of the first category leads to faster cell proliferation while mutations in genes of second category increases or changes their function. We propose MoonlightR a new approach to define TSGs and OCGs based on functional enrichment analysis, infer gene regulatory networks and upstream regulator analysis to score the importance of well-known biological processes with respect to the studied cancer.
The figure from Moonlight’s pipeline is shown below:
The proposed pipeline consists of following eight steps:
1 For the devel version of MoonlightR we use a short extract of 100 biological functions from QIAGEN’S Ingenuity Pathway Analysis (IPA). We are still working to integrate the package.
To install use the code below.
source("https://bioconductor.org/biocLite.R")
biocLite("MoonlightR")Please cite TCGAbiolinks package:
Related publications to this package:
Download: Get TCGA dataYou can search TCGA data using the getDataTCGA function.
getDataTCGA: Searching by cancer type and data type [Gene Expression]The user can query and download the cancer types supported by TCGA, using the function getDataTCGA: In this example we used LUAD gene expression data with only 10 samples to reduce time downloading.
dataFilt <- getDataTCGA(cancerType = "LUAD",
dataType = "Gene expression",
directory = "data",
nSample = 10)getDataTCGA: Searching by cancer type and data type [Methylation]The user can also query and download methylation data using the function getDataTCGA:
setwd("~/Dropbox/IB2_postdoc/Github/Moonlight/")
dataFilt <- getDataTCGA(cancerType = "TCGA-BRCA",
dataType = "Methylation",
directory = "data",nSample = 5)Download: Get GEO dataYou can search GEO data using the getDataGEO function.
GEO_TCGAtab a 18x12 matrix that provides the GEO data set we matched to one of the 18 given TCGA cancer types
knitr::kable(GEO_TCGAtab, digits = 2,
caption = "Table with GEO data set matched to one
of the 18 given TCGA cancer types ",
row.names = TRUE)| Cancer | TP | NT | DEG. | Dataset | TP.1 | NT.1 | Platform | DEG.. | Common | GEO_Normal | GEO_Tumor | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | BLCA | 408 | 19 | 2937 | GSE13507 | 165 | 10 | GPL65000 | 2099 | 896 | control | cancer |
| 2 | BRCA | 1097 | 114 | 3390 | GSE39004 | 61 | 47 | GPL6244 | 2449 | 1248 | normal | Tumor |
| 3 | CHOL | 36 | 9 | 5015 | GSE26566 | 104 | 59 | GPL6104 | 3983 | 2587 | Surrounding | Tumor |
| 4 | COAD | 286 | 41 | 3788 | GSE41657 | 25 | 12 | GPL6480 | 3523 | 1367 | N | A |
| 5 | ESCA | 184 | 11 | 2525 | GSE20347 | 17 | 17 | GPL571 | 1316 | 406 | normal | carcinoma |
| 6 | GBM | 156 | 5 | 4828 | GSE50161 | 34 | 13 | GPL570 | 4504 | 2660 | normal | GBM |
| 7 | HNSC | 520 | 44 | 2973 | GSE6631 | 22 | 22 | GPL8300 | 142 | 129 | normal | cancer |
| 8 | KICH | 66 | 25 | 4355 | GSE15641 | 6 | 23 | GPL96 | 1789 | 680 | normal | chromophobe |
| 9 | KIRC | 533 | 72 | 3618 | GSE15641 | 32 | 23 | GPL96 | 2911 | 939 | normal | clear cell RCC |
| 10 | KIRP | 290 | 32 | 3748 | GSE15641 | 11 | 23 | GPL96 | 2020 | 756 | normal | papillary RCC |
| 11 | LIHC | 371 | 50 | 3043 | GSE45267 | 46 | 41 | GPL570 | 1583 | 860 | normal liver | HCC sample |
| 12 | LUAD | 515 | 59 | 3498 | GSE10072 | 58 | 49 | GPL96 | 666 | 555 | normal | tumor |
| 13 | LUSC | 503 | 51 | 4984 | GSE33479 | 14 | 27 | GPL6480 | 3729 | 1706 | normal | squamous cell carcinoma |
| 14 | PRAD | 497 | 52 | 1860 | GSE6919 | 81 | 90 | GPL8300 | 246 | 149 | normal prostate | tumor samples |
| 15 | READ | 94 | 10 | 3628 | GSE20842 | 65 | 65 | GPL4133 | 2172 | 1261 | M | T |
| 16 | STAD | 415 | 35 | 2622 | GSE2685 | 10 | 10 | GPL80 | 487 | 164 | N | T |
| 17 | THCA | 505 | 59 | 1994 | GSE33630 | 60 | 45 | GPL570 | 1451 | 781 | N | T |
| 18 | UCEC | 176 | 24 | 4183 | GSE17025 | GPL570 | tp | lcm |
getDataGEO: Searching by cancer type and data type [Gene Expression]The user can query and download the cancer types supported by GEO, using the function getDataGEO:
dataFilt <- getDataGEO(GEOobject = "GSE20347",platform = "GPL571")dataFilt <- getDataGEO(TCGAtumor = "ESCA")Analysis: To analyze TCGA dataDPA: Differential Phenotype AnalysisDifferential Phenotype analysis is able to identify genes or probes that are significantly different between two phenotypes such as normal vs. tumor, or normal vs. stageI, normal vs. molecular subtype.
For gene expression data, DPA is running a differential expression analysis (DEA) to identify differentially expressed genes (DEGs) using the TCGAanalyze_DEA function from .
For methylation data DPA is running a differentially methylated regions analysis (DMR) to identify differentially methylated CpG sites using the TCGAanalyze_DMR the TCGAanalyze_DMR function from .
dataDEGs <- DPA(dataFilt = dataFilt,
dataType = "Gene expression")For gene expression data, DPA dealing with GEO data is running a differential expression analysis (DEA) to identify differentially expressed genes (DEGs) using to the eBayes and topTable functions from .
DataAnalysisGEO<- "../GEO_dataset/"
i<-5
cancer <- GEO_TCGAtab$Cancer[i]
cancerGEO <- GEO_TCGAtab$Dataset[i]
cancerPLT <-GEO_TCGAtab$Platform[i]
fileCancerGEO <- paste0(cancer,"_GEO_",cancerGEO,"_",cancerPLT, ".RData")
dataFilt <- getDataGEO(TCGAtumor = cancer)
GEOdegs <- DPA(dataConsortium = "GEO",
gset = dataFilt ,
colDescription = "title",
samplesType = c(GEO_TCGAtab$GEO_Normal[i],
GEO_TCGAtab$GEO_Tumor[i]),
fdr.cut = 0.01,
logFC.cut = 1,
gsetFile = paste0(DataAnalysisGEO,fileCancerGEO))We can visualize those differentially expressed genes (DEGs) with a volcano plot using the TCGAVisualize_volcano function from .
library(TCGAbiolinks)
TCGAVisualize_volcano(DEGsmatrix$logFC, DEGsmatrix$FDR,
filename = "DEGs_volcano.png",
x.cut = 7,
y.cut = 10^-5,
names = rownames(dataDEGs),
color = c("black","red","dodgerblue3"),
names.size = 2,
xlab = " Gene expression fold change (Log2)",
legend = "State",
title = "Volcano plot (Normal NT vs Tumor TP)",
width = 10)The figure resulted from the code above is shown below:
LPA: Literature Phenotype AnalysisThe user can perform a literature phenotype analysis using the function LPA.
data(DEGsmatrix)
DiseaseListNew <- list()
BPselected <- c("apoptosis","proliferation of cells")
for (i in 1:length(BPselected)){
BPannotations <- DiseaseList[[which(names(DiseaseList) == BPselected[i])]]$ID
dataLPA <- LPA(dataDEGs = DEGsmatrix,
BP = BPselected[i],
BPlist = BPannotations)
DiseaseListNew[[length(DiseaseListNew)+1]] <- dataLPA
names(DiseaseListNew)[[i]] <- BPselected[i]
}FEA: Functional Enrichment AnalysisThe user can perform a functional enrichment analysis using the function FEAcomplete. For each DEG in the gene set a z-score is calculated. This score indicates how the genes act in the gene set.
dataFEA <- FEA(DEGsmatrix = DEGsmatrix)The output can be visualized with a FEA plot.
FEAplot: Functional Enrichment Analysis PlotThe user can plot the result of a functional enrichment analysis using the function plotFEA. A negative z-score indicates that the process’ activity is decreased. A positive z-score indicates that the process’ activity is increased.
plotFEA(dataFEA = dataFEA, plotNAME = "FEAplot", height = 20, width = 10)The figure generated by the above code is shown below:
GRN: Gene Regulatory NetworkThe user can perform a gene regulatory network analysis using the function GRN which infers the network using the parmigene package.
dataGRN <- GRN(TFs = rownames(DEGsmatrix)[1:10], normCounts = dataFilt,
nGenesPerm = 10,kNearest = 3,nBoot = 10)URA: Upstream Regulator AnalysisThe user can perform upstream regulator analysis using the function URA. This function is applied to each DEG in the enriched gene set and its neighbors in the GRN.
data(dataGRN)
data(DEGsmatrix)
dataURA <- URA(dataGRN = dataGRN,
DEGsmatrix = DEGsmatrix,
BPname = NULL)PRA: Pattern Regognition AnalysisThe user can retrieve TSG/OCG candidates using either selected biological processes or a random forest classifier trained on known COSMIC OCGs/TSGs.
dataDual <- PRA(dataURA = dataURA,
BPname = c("apoptosis","proliferation of cells"),
thres.role = 0)The figure generated by the above code is shown below:
Running moonlight with a list of already validated TSG and OCG.
CosmicGenes <- c(knownDriverGenes$OCG, knownDriverGenes$TSG)
dataFilt <- getDataTCGA(cancerType = "BRCA",
dataType = "Gene expression",
directory = "data",
nSample = 10)
dataDEGs <- DPA(dataFilt = dataFilt,
dataType = "Gene expression")
dataFEA <- FEA(DEGsmatrix = dataDEGs)
dataGRN <- GRN(TFs = CosmicGenes,
DEGsmatrix = dataDEGs,
DiffGenes = TRUE,
normCounts = dataFilt)
dataURA <-URA(dataGRN = dataGRN,
DEGsmatrix = dataDEGs,
BPname = c("apoptosis",
"proliferation of cells"))
dataDual <-PRA(dataURA = dataURA,
BPname = c("apoptosis",
"proliferation of cells"),
thres.role = 1)plotNetworkHive: GRN hive visualization taking into account Cosmic cancer genesIn the following plot the nodes are separated into three groups: known tumor suppressor genes (yellow), known oncogenes (green) and the rest (gray).
data(knownDriverGenes)
plotNetworkHive(dataGRN, knownDriverGenes, 0.75)This vignette shows a complete workflow of the ‘MoonlightR’ package. The code is divided in 4 case study:
dataFilt <- getDataTCGA(cancerType = "LUAD",
dataType = "Gene expression",
directory = "data",
nSample = 10)
DEGsmatrix <- DPA(dataFilt = dataFilt,
dataType = "Gene expression")
dataFEA <- FEA(DEGsmatrix = dataDEGs)
dataGRN <- GRN(TFs = rownames(dataDEGs)[1:1000],
DEGsmatrix = dataDEGs,
DiffGenes = TRUE,
normCounts = dataFilt)
dataURA <-URA(dataGRN = dataGRN,
DEGsmatrix = DEGsmatrix,
BPname = c("apoptosis",
"proliferation of cells"))
dataDual <-PRA(dataURA = dataURA,
BPname = c("apoptosis",
"proliferation of cells"),
thres.role = 1)
CancerGenes <- list("TSG"=names(dataDual$TSG), "OCG"=names(dataDual$OCG))plotURA: Upstream regulatory analysis plotThe user can plot upstream regulatory analysis using the function plotURA.
plotURA(dataURA = dataURA[c(names(dataDual$TSG), names(dataDual$OCG)),],plotNAME = "URAplot")The figure resulted from the code above is shown below:
cancerList <- c("BLCA","COAD","ESCA","HNSC","STAD")
listMoonlight <- moonlight(cancerType = cancerList,
dataType = "Gene expression",
directory = "data",
nSample = 10,
nTF = 100,
DiffGenes = TRUE,
BPname = c("apoptosis","proliferation of cells"))
save(listMoonlight, file = paste0("listMoonlight_ncancer4.Rdata"))plotCircos: Moonlight Circos PlotThe results of the moonlight pipeline can be visualized with a circos plot. Outer ring: color by cancer type, Inner ring: OCGs and TSGs, Inner connections: green: common OCGs yellow: common TSGs red: possible dual role
plotCircos(listMoonlight = listMoonlight, additionalFilename = "_ncancer5")The figure resulted from the code above is shown below:
setwd("/Users/antoniocolaprico/Dropbox/IB2_postdoc/Github/Moonlight/")
load("/Users/antoniocolaprico/Dropbox/IB2_postdoc/Github/PackageTesting/TCGAbiolinks/data/geneInfo.rda")
load("/Users/antoniocolaprico/Downloads/BRCAlistMoonlight_stages-2.RData")
require(MoonlightR)
require(TCGAbiolinks)
listMoonlight <- NULL
for (i in 1:4){
dataDual <- moonlight(cancerType = "BRCA",
dataType = "Gene expression",
directory = "data",
nSample = 10,
nTF = 100,
DiffGenes = TRUE,
BPname = c("apoptosis","proliferation of cells"),
stage = i)
listMoonlight <- c(listMoonlight, list(dataDual))
save(dataDual, file = paste0("dataDual_stage",as.roman(i), ".Rdata"))
}
names(listMoonlight) <- c("stage1", "stage2", "stage3", "stage4")
# Prepare mutation's data for stages
mutation <- GDCquery_Maf(tumor = "BRCA")
dataClin <- GDCquery_clinic(project = "TCGA-BRCA",type = "clinical_patient")
dataClin <- GDCquery_clinic(project = "TCGA-HNSC",type = "clinical_patient")
res.mutation <- NULL
for(stage in 1:4){
curStage <- paste0("Stage ", as.roman(stage))
dataClin$tumor_stage <- toupper(dataClin$tumor_stage)
dataClin$tumor_stage <- gsub("[ABCDEFGH]","",dataClin$tumor_stage)
dataClin$tumor_stage <- gsub("ST","Stage",dataClin$tumor_stage)
dataStg <- dataClin[dataClin$tumor_stage %in% curStage,]
message(paste(curStage, "with", nrow(dataStg), "samples"))
dataSmTP <- mutation$Tumor_Sample_Barcode
dataStgC <- dataSmTP[substr(dataSmTP,1,12) %in% dataStg$bcr_patient_barcode]
dataSmTP <- dataStgC
info.mutation <- mutation[mutation$Tumor_Sample_Barcode %in% dataSmTP,]
ind <- which(info.mutation[,"Consequence"]=="inframe_deletion")
ind2 <- which(info.mutation[,"Consequence"]=="inframe_insertion")
ind3 <- which(info.mutation[,"Consequence"]=="missense_variant")
res.mutation <- c(res.mutation, list(info.mutation[c(ind, ind2, ind3),c(1,51)]))
}
names(res.mutation) <- c("stage1", "stage2", "stage3", "stage4")
tmp <- NULL
tmp <- c(tmp, list(listMoonlight[[1]][[1]]))
tmp <- c(tmp, list(listMoonlight[[2]][[1]]))
tmp <- c(tmp, list(listMoonlight[[3]][[1]]))
tmp <- c(tmp, list(listMoonlight[[4]][[1]]))
names(tmp) <- names(listMoonlight)
mutation <- GDCquery_Maf(tumor = "BRCA")
plotCircos(listMoonlight=listMoonlight,listMutation=res.mutation, additionalFilename="proc2_wmutation", intensityColDual=0.2,fontSize = 2)The results of the moonlight pipeline can be visualized with a circos plot. Outer ring: color by cancer type, Inner ring: OCGs and TSGs, Inner connections: green: common OCGs yellow: common TSGs red: possible dual role
The figure generated by the code above is shown below:
In this section we showed downstream analysis with replication of Moonlight in TCGA’s data within comparison of different molecular subtypes and normal samples. You can use the function TCGAquery_subtype from TCGAbiolinks to retrieve this information.
The Cancer Genome Atlas (TCGA) Research Network has reported integrated genome-wide studies of various diseases. We have added some of the subtypes defined by these report in our package. The ACC(Cancer Genome Atlas Research Network and others 2016), BRCA (Cancer Genome Atlas Research Network and others 2012c), COAD (Cancer Genome Atlas Research Network and others 2012b), GBM (Ceccarelli, Michele and Barthel, Floris P and Malta, Tathiane M and Sabedot, Thais S and Salama, Sofie R and Murray, Bradley A and Morozova, Olena and Newton, Yulia and Radenbaugh, Amie and Pagnotta, Stefano M and others 2016), HNSC (Cancer Genome Atlas Research Network and others 2015a), KICH (Davis, Caleb F and Ricketts, Christopher J and Wang, Min and Yang, Lixing and Cherniack, Andrew D and Shen, Hui and Buhay, Christian and Kang, Hyojin and Kim, Sang Cheol and Fahey, Catherine C and others 2014), KIRC(Cancer Genome Atlas Research Network and others 2013a), KIRP (Linehan, W Marston and Spellman, Paul T and Ricketts, Christopher J and Creighton, Chad J and Fei, Suzanne S and Davis, Caleb and Wheeler, David A and Murray, Bradley A and Schmidt, Laura and Vocke, Cathy D and others 2016), LGG (Ceccarelli, Michele and Barthel, Floris P and Malta, Tathiane M and Sabedot, Thais S and Salama, Sofie R and Murray, Bradley A and Morozova, Olena and Newton, Yulia and Radenbaugh, Amie and Pagnotta, Stefano M and others 2016), LUAD (Cancer Genome Atlas Research Network and others 2014b), LUSC(Cancer Genome Atlas Research Network and others 2012a), PRAD(Cancer Genome Atlas Research Network and others 2015c), READ (Cancer Genome Atlas Research Network and others 2012b), SKCM (Cancer Genome Atlas Research Network and others 2015b), STAD (Cancer Genome Atlas Research Network and others 2014a), THCA (Cancer Genome Atlas Research Network and others 2014c), UCEC (Cancer Genome Atlas Research Network and others 2013b) tumors have data added.
A subset of the lgg subytpe is shown below:
## Subtype information from:doi:10.1016/j.cell.2015.12.028
| patient | Tissue.source.site | Study | BCR | |
|---|---|---|---|---|
| 1 | TCGA-CS-4938 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 2 | TCGA-CS-4941 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 3 | TCGA-CS-4942 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 4 | TCGA-CS-4943 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 5 | TCGA-CS-4944 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 6 | TCGA-CS-5390 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 7 | TCGA-CS-5393 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 8 | TCGA-CS-5394 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 9 | TCGA-CS-5395 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 10 | TCGA-CS-5396 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
In this section we showed downstream analysis with replication of Moonlight in TCGA’s data within comparison of different molecular subtypes and normal samples. You can use the function GDCquery_Maf from TCGAbiolinks to retrieve this information.
A subset of the lgg subytpe is shown below:
## Subtype information from:doi:10.1016/j.cell.2015.12.028
| patient | Tissue.source.site | Study | BCR | |
|---|---|---|---|---|
| 1 | TCGA-CS-4938 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 2 | TCGA-CS-4941 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 3 | TCGA-CS-4942 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 4 | TCGA-CS-4943 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 5 | TCGA-CS-4944 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 6 | TCGA-CS-5390 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 7 | TCGA-CS-5393 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 8 | TCGA-CS-5394 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 9 | TCGA-CS-5395 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
| 10 | TCGA-CS-5396 | Thomas Jefferson University | Brain Lower Grade Glioma | IGC |
Session Information ******
sessionInfo()## R version 3.3.1 (2016-06-21)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.11.1 (El Capitan)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] TCGAbiolinks_2.1.11 png_0.1-7 MoonlightR_0.99.1
## [4] doParallel_1.0.10 iterators_1.0.8 foreach_1.4.3
## [7] BiocStyle_2.1.33
##
## loaded via a namespace (and not attached):
## [1] circlize_0.3.9
## [2] fastmatch_1.0-4
## [3] aroma.light_3.3.2
## [4] plyr_1.8.4
## [5] igraph_1.0.1
## [6] ConsensusClusterPlus_1.37.0
## [7] splines_3.3.1
## [8] BiocParallel_1.7.8
## [9] TH.data_1.0-7
## [10] GenomeInfoDb_1.9.13
## [11] ggplot2_2.1.0
## [12] digest_0.6.10
## [13] BiocInstaller_1.23.9
## [14] htmltools_0.3.5
## [15] GOSemSim_1.99.4
## [16] GO.db_3.4.0
## [17] gdata_2.17.0
## [18] magrittr_1.5
## [19] memoise_1.0.0
## [20] cluster_2.0.4
## [21] limma_3.29.21
## [22] ComplexHeatmap_1.11.7
## [23] Biostrings_2.41.4
## [24] readr_1.0.0
## [25] annotate_1.51.1
## [26] matrixStats_0.50.2
## [27] R.utils_2.4.0
## [28] sandwich_2.3-4
## [29] jpeg_0.1-8
## [30] colorspace_1.2-6
## [31] rvest_0.3.2
## [32] ggrepel_0.5
## [33] dplyr_0.5.0
## [34] jsonlite_1.1
## [35] hexbin_1.27.1
## [36] RCurl_1.95-4.8
## [37] graph_1.51.0
## [38] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [39] roxygen2_5.0.1
## [40] supraHex_1.11.2
## [41] genefilter_1.55.2
## [42] GEOquery_2.39.4
## [43] ape_3.5
## [44] zoo_1.7-13
## [45] survival_2.39-5
## [46] gtable_0.2.0
## [47] zlibbioc_1.19.0
## [48] XVector_0.13.7
## [49] GetoptLong_0.1.5
## [50] kernlab_0.9-24
## [51] Rgraphviz_2.17.0
## [52] shape_1.4.2
## [53] prabclus_2.2-6
## [54] BiocGenerics_0.19.2
## [55] DEoptimR_1.0-6
## [56] scales_0.4.0
## [57] DOSE_2.99.0
## [58] HiveR_0.2.55
## [59] DESeq_1.25.0
## [60] mvtnorm_1.0-5
## [61] edgeR_3.15.2
## [62] DBI_0.5-1
## [63] GGally_1.2.0
## [64] ggthemes_3.2.0
## [65] Rcpp_0.12.7
## [66] xtable_1.8-2
## [67] matlab_1.0.2
## [68] mclust_5.2
## [69] preprocessCore_1.35.0
## [70] stats4_3.3.1
## [71] httr_1.2.1
## [72] fgsea_0.99.7
## [73] gplots_3.0.1
## [74] RColorBrewer_1.1-2
## [75] fpc_2.1-10
## [76] modeltools_0.2-21
## [77] reshape_0.8.5
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## [79] R.methodsS3_1.7.1
## [80] flexmix_2.3-13
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## [116] lattice_0.20-34
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## [119] GlobalOptions_0.0.10
## [120] data.table_1.9.6
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## [122] parmigene_1.0.2
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## [126] qvalue_2.5.2
## [127] R6_2.2.0
## [128] latticeExtra_0.6-28
## [129] affy_1.51.1
## [130] hwriter_1.3.2
## [131] ShortRead_1.31.1
## [132] KernSmooth_2.23-15
## [133] gridExtra_2.2.1
## [134] IRanges_2.7.16
## [135] codetools_0.2-15
## [136] MASS_7.3-45
## [137] gtools_3.5.0
## [138] assertthat_0.1
## [139] chron_2.3-47
## [140] SummarizedExperiment_1.3.82
## [141] rjson_0.2.15
## [142] withr_1.0.2
## [143] GenomicAlignments_1.9.6
## [144] Rsamtools_1.25.2
## [145] multcomp_1.4-6
## [146] S4Vectors_0.11.18
## [147] diptest_0.75-7
## [148] clusterProfiler_3.1.8
## [149] tidyr_0.6.0
## [150] class_7.3-14
## [151] rmarkdown_1.0
## [152] Biobase_2.33.3
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